{"id":"W2475020744","doi":"10.1109/icuas.2016.7502546","title":"Vision-based forest fire detection in aerial images for firefighting using UAVs","year":2016,"lang":"en","type":"article","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Firefighting; Artificial intelligence; Computer science; Optical flow; Computer vision; Feature (linguistics); Fire detection; Thresholding; Pixel; Remote sensing; Image (mathematics); Geography; Engineering; Cartography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001544243,0.0001159727,0.0001344846,0.00009359157,0.00006770265,0.00002696184,0.00005311995,0.00009359736,0.0000484419],"category_scores_gemma":[0.00006440514,0.00008462428,0.00006784188,0.0001249634,0.00001203404,0.0001489968,0.000006666728,0.00004334944,0.00001793744],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001419953,"about_ca_system_score_gemma":0.00001117853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001419889,"about_ca_topic_score_gemma":0.0004872449,"domain_scores_codex":[0.9992919,0.00001735406,0.0002493172,0.0001455691,0.00008219578,0.0002136603],"domain_scores_gemma":[0.9996439,0.0001394803,0.00002657951,0.0001205944,0.00002901088,0.0000404089],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001386565,0.000026266,0.001411646,0.0002003756,0.00001886848,0.000004010004,0.00007004636,0.0333259,0.7219741,0.00003018718,0.0005860762,0.2422138],"study_design_scores_gemma":[0.001288973,0.00006036142,0.002141845,0.0001367918,0.000004164998,0.000004686788,0.00002213778,0.8493732,0.1402555,0.00004113146,0.006467202,0.000204036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7024435,0.00003235939,0.2947769,0.00008718928,0.001336064,0.0003197149,0.00001158567,0.0004226401,0.0005700137],"genre_scores_gemma":[0.9985714,0.000002715352,0.0009675318,0.00001535569,0.0002344213,0.00003882216,0.000001466714,0.00003230411,0.0001359808],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8160473,"threshold_uncertainty_score":0.3450878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01059584265696785,"score_gpt":0.2285963781090632,"score_spread":0.2180005354520954,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}